Sound speed profiles in high spatiotemporal resolution using multigrid three-dimensional variational method: a coastal experiment off northern Shandong Peninsula

It is essential to acquire sound speed profiles (SSPs) in high-precision spatiotemporal resolution for undersea acoustic activities. However, conventional observation methods cannot obtain high-resolution SSPs. Besides, SSPs are complex and changeable in time and space, especially in coastal areas. We proposed a new space-time multigrid three-dimensional variational method with weak constraint term (referred to as STC-MG3DVar) to construct high-precision spatiotemporal resolution SSPs in coastal areas, in which sound velocity is defined as the analytical variable, and the Chen-Millero sound velocity empirical formula is introduced as a weak constraint term into the cost function of the STC-MG3DVar. The spatiotemporal correlation of sound velocity observations is taken into account in the STC-MG3DVar method, and the multi-scale information of sound velocity observations from long waves to short waves can be successively extracted. The weak constraint term can optimize sound velocity by the physical relationship between sound velocity and temperature-salinity to obtain more reasonable and accurate SSPs. To verify the accuracy of the STC-MG3DVar, SSPs observations and CTD observations (temperature observations, salinity observations) are obtained from field experiments in the northern coastal area of the Shandong Peninsula. The average root mean square error (RMSE) of the STC-MG3DVar-constructed SSPs is 0.132 m/s, and the STC-MG3DVar method can improve the SSPs construction accuracy over the space-time multigrid 3DVar without weak constraint term (ST-MG3DVar) by 10.14% and over the spatial multigrid 3DVar with weak constraint term (SC-MG3DVar) by 44.19%. With the advantage of the constraint term and the spatiotemporal correlation information, the proposed STC-MG3DVar method works better than the ST-MG3DVar and the SC-MG3DVar in constructing high-precision spatiotemporal resolution SSPs.

     

基于时空四维多尺度分析方法的卫星遥感和现场观测联合同化研究

充分融合使用卫星遥感与现场观测信息,构建高质量的水下温盐场是海洋科学研究发展的前沿课题。目前,绝大多数同化系统使用的同化方案,均需要假设要素在海表与水下存在某种人为预先设定的关系,从而导致得到的温盐分析场人为性较强,不能完全客观地反映真实的海洋状态。本研究提出了一种不做任何关系假设,仅依靠不同种类的观测资料在时间和空间上的相互补充作用,融合卫星遥感与现场观测资料,进行时空四维多尺度分析的方案。通过与分别单独同化这两类观测资料的试验结果相比,该方案既可以得到较精准的温盐剖面结构特征,又能够反映出海面中尺度变化的细节信息,最大化地提取了观测资料中的多尺度信息,实现了“1 + 1 > 2”的效果,构建了完全客观的温盐分析场。研究结果还表明,同化卫星遥感海表面盐度资料可以有效改善温度和盐度的分析,证明了海表面盐度观测在温盐同化中的重要性。     

基于解析四维集合变分的全球正压谱模式初始场优化研究

解析四维集合变分数据同化方法是一种无需伴随的、完全继承四维变分非线性处理能力的、“流依赖”的非顺序数据同化方法。本研究基于解析四维集合变分,开展全球正压谱模式初始场优化研究,验证解析四维集合变分的同化能力,并构建了高效的扰动集合成员生成方案,将解析四维集合变分降维到了样本空间,最后检验了解析四维集合变分对同化积分窗口长度和观测采样间隔的敏感性。试验结果表明,解析四维集合变分能优化全球正压谱模式的初始场,降维到样本空间后,只需要80个集合成员就可以取得很好的同化效果,在较长的同化积分窗口和观测采样间隔的条件下也可以达到理想的同化效果。     

基于演化算符的南海海面高度异常中长期统计预报

水下移动平台行动时需要1～3个月左右海洋数值预测预报结果,但是当前数值预报技术受对应的气象驱动场预报时效的限制,难以提供10 d以上的数值预报产品。鉴于海水在动力热力上具有较大的惯性,海洋内区有其自身的演化规律,本研究设计了一种基于演化算符的统计预测方法,利用历史卫星遥感资料构建海洋状态变量中长期演化矩阵,并结合惯性预报模型,构建了最终的南海海洋中长期统计预报模型,能够提供1～60 d逐日的南海海面高度异常预测结果,开展数值试验验证了该方法的有效性,结果表明,在起报后15 d内,预报结果与卫星资料的相关系数均大于0.8,在起报60 d内,相关系数仍高于0.6。     

An application of the A-4DEnVar to coupled parameter optimization

In variational methods, coupled parameter optimization (CPO) often needs a long minimization time window (MTW) to fully incorporate observational information, but the optimal MTW somehow depends on the model nonlinearity. The analytical four-dimensional ensemble-variational (A-4DEnVar) considers model nonlinearity well and avoids adjoint model. It can theoretically be applied to CPO. To verify the feasibility and the ability of the A-4DEnVar in CPO, “twin” experiments based on A-4DEnVar CPO are conducted for the first time with the comparison of four-dimensional variational (4D-Var). Two algorithms use the same background error covariance matrix and optimization algorithm to control variates. The experiments are based on a simple coupled ocean-atmosphere model, in which the atmospheric part is the highly nonlinear Lorenz-63 model, and the oceanic part is a slab ocean model. The results show that both A-4DEnVar and 4D-Var can effectively reduce the error of state variables through CPO. Besides, two methods produce almost the same results in most cases when the MTW is less than 560 time steps. The results are similar when the MTW is larger than 560 time steps and less than 880 time steps. The largest MTW of 4D-Var and A-4DEnVar are 1 200 time steps. Moreover, A-4DEnVar is not sensitive to ensemble size when the MTW is less than 720 time steps. A-4DEnVar obtains satisfactory results in the case of highly nonlinear model and long MTW, suggesting that it has the potential to be widely applied to realistic CPO.